Tweetovi
- Tweetovi, trenutna stranica.
- Tweetovi i odgovori
- Medijski sadržaj
Blokirali ste korisnika/cu @ml_perception
Jeste li sigurni da želite vidjeti te tweetove? Time nećete deblokirati korisnika/cu @ml_perception
-
Prikvačeni tweet
Excited to share our work on BART, a method for pre-training seq2seq models by de-noising text. BART outperforms previous work on a bunch of generation tasks (summarization/dialogue/QA), while getting similar performance to RoBERTa on SQuAD/GLUE
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
At NeurIPS to present our work on agents that "think in language" by generating and then executing plans in the form of natural language instructions. Code and a large new dataset are online! https://arxiv.org/abs/1906.00744 https://twitter.com/facebookai/status/1171090891072688128 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mike Lewis proslijedio/la je Tweet
Generalization through Memorization: Nearest Neighbor Language Models Reduces ppl from 18.27 to 15.79 (sota) in Wikitext-103 using kNN and pretrained Wikitext LM without further training. https://arxiv.org/abs/1911.00172
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Improve your language model by converting it into a deep nearest neighbour classifier! The amazing
@ukhndlwl pushes SOTA on Wikitext-103 by nearly 3 points, without any additional training (and gets a few other surprising results too). https://arxiv.org/abs/1911.00172 https://twitter.com/ukhndlwl/status/1191188235629711360 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
-
Joint work with
@YinhanL Naman Goyal@gh_marjan Abdelrahman Mohamed,@omerlevy_@vesko_st@LukeZettlemoyerPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
This does seem closely related to T5 from Google last week. I haven't read that in detail yet, but it seems like we use a slightly different pre-training objective, and better results for the same model size. We haven't tried training an 11B parameter model yet, though :-)
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I found the summarization performance surprisingly good - BART does seem to be able to combine information from across a whole document with background knowledge to produce highly abstractive summaries. Some typical examples beneath:pic.twitter.com/EENDPgTqrl
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mike Lewis proslijedio/la je Tweet
Impressed by Ovid's
but want a deeper eval of GPT2 open-ended NLG?
See our @conll2019 paper "Do Massively Pretrained Language Models Make Better Storytellers?" https://arxiv.org/abs/1909.10705 Work with@aneeshpappu@RohunSaxena@yakhila_04@stanfordnlp#NLProc#DeepLearning#AIPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.